Periurban areas of growing cities in developing countries have been conceptualised as highly dynamic landscapes characterised by a mixture of socioeconomic structures, land uses and functions. While the body of conceptual literature on periurban areas has significantly increased over the past two decades, methods for operationalising these multi-dimensional concepts are rather limited. Yet, information about the location and areal extent of periurban areas is needed for integrated planning in the urban–rural interface. This article presents the results of a study aiming at classifying and mapping periurban areas along the urban–rural gradient of Tamale, a medium-sized city in Ghana. The study used a quantitative, multi-dimensional methodology involving the following as core elements: (1) a relative measure of how urban a place and its people are in terms of services, infrastructure and livelihoods (urbanicity index); (2) the diversity of households regarding their livelihoods and access to urban services; and (3) land use dynamics. Therefore, data from a household survey, as well as land use and other secondary geospatial data were collected and analysed at different spatial scales. The findings suggested that the periurban space consists of two main zones. Inner periurban areas are driven by urban expansion and the conversion of non-urban into urban land use is most visible here. These areas exhibit higher levels of socioeconomic diversity, compared to both rural and urban areas. Outer periurban areas are less dynamic in terms of land use change and exhibit lower building densities, and compared with rural areas, hold stronger links to the city related to the movement of people and goods. The spatial analysis revealed that periurban areas develop mainly along major transport corridors across administrative divisions, as well as in the form of periurban islands in the rural zone. This study set out to extend existing methodologies to map urban and periurban development in medium-sized cities in sub-Saharan Africa, useful for urban and regional planning beyond administrative boundaries.

For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m², 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km² study area substantiates our findings.

Background: Tickborne-encephalitis (TBE) is a potentially life-threating neurological disease that is mainly transmitted by ticks. The goal of the present study is to analyze the potential uniform environmental patterns of the identified TBEV microfoci in Germany. The results are used to calculate probabilities for the present distribution of TBEV microfoci in Germany based on a geostatistical model. Methods: We aim to consider the specification of environmental characteristics of locations of TBEV microfoci detected in Germany using open access epidemiological, geographical and climatological data sources. We use a two-step geostatistical approach, where in a first step, the characteristics of a broad set of environmental variables between the 56 TBEV microfoci and a control or comparator set of 3575 sampling points covering Germany are compared using Fisher’s Exact Test. In the second step, we select the most important variables, which are then used in a MaxEnt distribution model to calculate a high resolution (400 × 400 m) probability map for the presence of TBEV covering the entire area of Germany. Results: The findings from the MaxEnt prediction model indicate that multi annual actual evapotranspiration (27.0%) and multi annual hot days (22.5%) have the highest contribution to our model. These two variables are followed by four additional variables with a lower, but still important, explanatory influence: Land cover classes (19.6%), multi annual minimum air temperature (14.9%), multi annual sunshine duration (9.0%), and distance to coniferous and mixed forest border (7.0%). Conclusions: Our findings are based on defined TBEV microfoci with known histories of infection and the repeated confirmation of the virus in the last years, resulting in an in-depth high-resolution model/map of TBEV microfoci in Germany. Multi annual actual evapotranspiration (27%) and multi annual hot days (22.5%) have the most explanatory power in our model. The results may be used to tailor specific regional preventive measures and investigations.

Information on individual hazard perception while cycling and the associated feeling of safety are key aspects to foster sustainable urban cycling mobility. Although cyclist’s perceptions must also be critically reviewed, such crowdsourced Volunteered Geographic Information (VGI) provides wide-ranging insights on diverse hazard categories in cycling. In this case study in the city of Freiburg, Germany, hazard perceptions, information about lane types, and the underlying routes were crowdsourced via an open source smartphone application by a small group with the aim of providing cyclists with effective solutions. By dealing with levels of reliability, we show that even a small group of laypersons can generate an extensive and valuable set of VGI consisting of comprehensive hazard categories. We demonstrate that (1) certain hazards are interlinked to specific lane types, and (2) the individual hazard perceptions and objective parameters, i.e., accident data, are often congruent spatially; consequently, (3) dangerous hot spots can be derived. By considering cyclists’ needs, this approach outlines how a people-based perspective can supplement regional planning on the local scale.

The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather patterns were explored as there were aspects not documented by official damage reports or derived from official weather data. We analysed Tweets regarding the severe storm event Friederike to map their spatio-temporal patterns. More than 50% of the retrieved >23.000 tweets were geocoded by applying supervised information retrievals, text mining, and geospatial analysis methods. Complementary, central topics were clustered and linked to official weather data for cross-evaluation. The data confirmed (1) a scale-dependent relationship between the wind speed and the societal echo. In addition, the study proved that (2) reporting activity is moderated by population distribution. An in-depth analysis of the crowds’ central topic clusters in response to the storm Friederike (3) revealed a plausible sequence of dominant communication contents during the severe weather event. In particular, the merge of the studied AGI and other environmental datasets at different spatio-temporal scales shows how such user-generated content can be a useful complementary data source to study severe weather events and the ensuing societal echo.